Blockchain Technology Adoption in Canadian Pharmaceutical Sectors: An empirical analysis for a future outlook
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There are many calls in the literature to investigate the Blockchain technology adoption (BCT) in Canadian Organizations and its impact on boosting enterprises' competitive advantages. Although the literature requires more research cases, it is more timely and relevant that the analysis be done as early as today. Various empirical supports for Technology Acceptance Model (TAM) are available depending on situation specifics. TAM remains a widespread and convenient theoretical framework for examination of aspects contributing to technology acceptance. This study aims to find the driving forces that effectively illustrate the blockchain technology adoption in Canadian Pharmaceutical Organizations and to be able to face the challenges associated with the process of adoption. This study examined BCT application using contacts from Canadian Companies Capabilities directory (CCC) and applied SEM regression using AMOS software with 750 respondents from pharmaceutical businesses using TAM framework. Path analysis results were good: chi2 (4918.592), chi2 / DF (5.513), RMSEA (0.049), CFI (0.753), and TLI (0.804). Perceived ease of use, Perceived Usefulness, attitude towards use, and intention to use predicted BCT utilization, yet two relationships (i.e., PEOU->PU and PU->IU) were rejected in the tested model as they show negative conformity results. All components explain more than 50% of variation, hence presenting a reasonable fit between the data examined and the research model. These findings will help in understanding of pharmaceutical organizations' adoption of BCT for researchers, regulators and developers and providing supported evidence on factors contributing to the adoption of BCT in Canadian Organizations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it